Classifying and clustering noisy images using subset learning based on convolutional neural networks

Shuen Lin Jeng, Yin Chen Chang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A high yield rate is a key factor related to success in the competitive global semiconductor manufacturing business market. Wafer bin maps (WBMs) can be used as one measure of the output quality of a semiconductor manufacturing process. A WBM is the image results from a number of circuit probe (CP) tests on a wafer after the completion of a manufacturing process. The specific defect patterns on WBMs provide crucial information for engineers to trace the causes of defects in the complicated manufacturing process. This study is aimed toward investigating major practical challenges in current WBM analyses. Our approach involved utilizing a small, carefully labeled subset to reduce the labor requirements and human recognition bias related to identification of very noisy images. The first proposed procedure classified the noisy defect patterns by using convolutional neural networks (CNNs) trained with a small subset of labeled WBMs in the early batches. The second proposed procedure provided the proper clusters of noisy defect patterns using the features extracted from the trained CNNs. This procedure made it possible to generate various clusters of WMBs and integrate them in label space. The third procedure separated the new pattern from the existing defect patterns with the help of a supplemental dataset from a similar wafer product. The evaluation of the three proposed procedures was done with simulation data generated from real sets of WBMs, which were added with random noise to maintain confidentiality. The evaluation results demonstrated the practical validity of the proposed procedures.

Original languageEnglish
Pages (from-to)2343-2364
Number of pages22
JournalQuality and Reliability Engineering International
Issue number6
Publication statusAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


Dive into the research topics of 'Classifying and clustering noisy images using subset learning based on convolutional neural networks'. Together they form a unique fingerprint.

Cite this